2026's Seminars
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[ELEN] 2026-01-27 (14:00) : The MPC-in-the-Head Paradigm for Post-Quantum Signatures: Recent Frameworks and Applications
At Shannon
Speaker :
Thibauld Feneuil (CryptoExperts)
Abstract : Signature schemes based on the MPC-in-the-Head (MPCitH) paradigm play a central role in post-quantum cryptography, enabling constructions from a broad range of hardness assumptions. In NIST's ongoing process for additional post-quantum digital signature standards, six candidate schemes rely on MPCitH, spanning assumptions from symmetric-key, code-based, and multivariate cryptography.
In this seminar, the speaker will first recall the core idea behind MPC-in-the-Head. They then will discuss how the paradigm has evolved into the recent VOLE-in-the-Head and TC-in-the-Head frameworks through the lens of Polynomial IOPs. Introduced in 2023, these frameworks underpin today’s MPCitH-based NIST candidates and enable signature sizes in the 2.5–5 kB range. The seminar will conclude by positioning MPCitH schemes within the broader landscape of post-quantum signatures, and by highlighting current efforts to port MPCitH-based implementations to embedded platforms.
[INGI] 2026-01-23 (11:00) : Neuro-symbolic Deep Learning with Requirements
At Shannon, Maxwell a.105
Speaker :
Eleonora Giunchiglia (Assistant Professor in Machine Learning and AI at Imperial College London | PI of DUCK Lab)
Abstract : For their outstanding ability of finding hidden patterns in data, deep learning models have been extensively applied in many different domains. However, recent works have shown that, if a set of requirements expressing inherent knowledge about the problem at hand is given, then neural networks often fail to comply with them. This represents a major drawback for deep learning models, as requirements compliance is normally considered a necessary condition for standard software deployment. We present a neuro-symbolic framework able to make any neural network compliant by design to a given set of requirements over the output space expressed in full propositional logic. This framework integrates the requirements into the output layer of the neural network.
[INMA] 2026-01-20 (14:00) : Opinion Dynamics with Nonlinear Interaction: From Robust Clustering to Environmental Coupling
At Euler building (room A.002)
Speaker :
Anthony Couthures (Université de Lorraine)
Abstract : Social opinion and environmental states are deeply coupled: collective human behavior impacts the environment, while the state of the environment feeds back into public opinion. In this talk, I will present a mathematical framework to analyze these interactions, moving from standard consensus models to coupled socio-environmental dynamics.
First, I will introduce a generalized framework for multi-agent opinion dynamics with nonlinear interactions. Unlike classical linear consensus models, nonlinear communication allows for the emergence of rich behaviors beyond simple agreement. We will establish a sharp threshold linking network connectivity (algebraic connectivity) and interaction nonlinearity (Lipschitz constant) that dictates the transition from global synchronization to persistent polarization. Using Input-to-State Stability (ISS) theory, I will also discuss the robustness of these polarized clusters against external influence.
In the second part, I will couple this opinion model with an environmental resource variable. By analyzing the system on the synchronization manifold, we identify the role of the "attention parameter" β: representing the weight agents place on environmental feedback versus social influence. Through bifurcation analysis, I will demonstrate how varying this parameter triggers fundamental qualitative changes, specifically Pitchfork bifurcations (leading to bistability and polarization) and Hopf bifurcations (leading to recurrent cycles of environmental collapse and recovery).
[ELEN] 2026-01-06 (09:00) : Environmental Net Impact of AI - Reviewing Challenges for Decision Making
At Shannon
Speaker :
Daniel Schien (University of Bristol)
Abstract : The net environmental impact of digital services, and artificial intelligence (AI) services in particular, is of increasing interest to society and governance of this part of the economy. The practice of weighing of both positive and negative consequences to arrive at a net impact of digital services has been around for many years and attracted controversy. Efforts such as methodology development through the EU Green Digital Coalition demonstrate ongoing interest as well as the fundamental challenges with this practice. Following a recent pre-print article, I will review existing net-impact methodologies along with the general epistemological challenges and illustrate them through a case study of estimating the benefits from substituting human translation work through LLMs.
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